AAAI Publications, Twenty-Eighth AAAI Conference on Artificial Intelligence

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Fast and Accurate Influence Maximization on Large Networks with Pruned Monte-Carlo Simulations
Naoto Ohsaka, Takuya Akiba, Yuichi Yoshida, Ken-ichi Kawarabayashi

Last modified: 2014-06-19


Influence maximization is a problem to find small sets of highly influential individuals in a social network to maximize the spread of influence under stochastic cascade models of propagation. Although the problem has been well-studied, it is still highly challenging to find solutions of high quality in large-scale networks of the day. While Monte-Carlo-simulation-based methods produce near-optimal solutions with a theoretical guarantee, they are prohibitively slow for large graphs. As a result, many heuristic methods without any theoretical guarantee have been developed, but all of them substantially compromise solution quality. To address this issue, we propose a new method for the influence maximization problem. Unlike other recent heuristic methods, the proposed method is a Monte-Carlo-simulation-based method, and thus it consistently produces solutions of high quality with the theoretical guarantee. On the other hand, unlike other previous Monte-Carlo-simulation-based methods, it runs as fast as other state-of-the-art methods, and can be applied to large networks of the day. Through our extensive experiments, we demonstrate the scalability and the solution quality of the proposed method.


influence maximization; independent cascade model; social networks

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